Amanda Hulse-Kemp

We seek to understand the genetic basis of non-race-specific resistance to fusiform rust disease caused by Cronartium quercuum f. sp. fusiforme (Cqf) in Pinus taeda, an economically critical pine species. In previous research, our group mapped two major resistance QTL with high genetic resolution in the genome of a P. taeda resistance donor. In a parallel bulked-segregant RNAseq experiment, we identified candidate resistance genes with SNP highly associated with resistance to Cqf. These genes were part of the nucleotide-binding leucine-rich repeat. Here, we will leverage our newly gained knowledge of the genetics of host resistance to generate a pine population segregating for the same two resistance QTL. To understand the genetics of avirulence in the pathogen, the pine population will then be challenged with a diverse basidiospore mixture of Cqf in an artificial inoculation experiment. Following symptom development, fungal strains capable of growing on each of four host resistance genotypes will be sampled directly from diseased tissue and sequenced. Following SNP discovery, the fungal genome will be scanned for the presence of selective sweeps that would indicate proximity to genes selected for virulence against one or the other QTL, such as effectors.

Graduate fellowship will support research involving identifying historically important genes in cotton through deep sequencing and data analysis. The data will be utilized to build new tools for cotton breeding and research activities using cutting edge algorithms and big data sets.

The turfgrass industry is a multibillion dollar industry in the United States, and represents tens of thousands of jobs related to production, installation, and management of turfgrasses. Outside of its significant economic impacts, turfgrasses also provide significant environmental and social benefits. Despite these numerous benefits, the turfgrass industry faces many serious challenges. Greatest among these is the limited availability and reduced quality of water for irrigating turfgrass areas. Although sustainable landscapes are a concern throughout the country, severe droughts and limited water in California and the Southwestern United States are already forcing changes to the landscape. Due to increasingly limited water resources and the desire to have more sustainable landscapes, there is a growing need for turfgrasses which can withstand drought conditions. A transdisciplinary group from North Carolina State University (NCSU), Oklahoma State University (OSU), Texas A&M AgriLife Research (TAMUS), the University of Georgia (UGA), and the University of Florida (UF) was formed to address these problems and to develop turfgrasses with reduced irrigation requirements for use in southern landscapes. These efforts have focused on economically important warm-season turfgrass species, bermudagrass [Cynodon spp. (L.) Rich], zoysiagrass (Zoysia spp. Steud.), St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze], and seashore paspalum (Paspalum vaginatum Swartz), that have the potential to significantly reduce water use in future landscapes. To date eight improved cultivars have been released from this project. However, additional efforts are needed to continue the development pipeline for new cultivars and genetic tools, promote their adoption, and quantify their impact. Advancing this successful research relationship will allow for efficient progress towards improved screening methods and molecular markers using new tools and technologies, the dissemination of important information to stakeholders and end-users, and ultimately allow for the continued utilization of turfgrasses in sustainable landscapes.

Current plant breeding programs generate a disproportional amount of data in relationship to their ability to analyze and implement strategies for selection. For this reason and the lack of expertise in the area, plant breeding programs need to bridge the computational and programming acuity of computer scientists to harness the power of the data collected. Furthermore, pairing objective data collection methods in a high-throughput phenotyping approach provides consistency and efficiency, drastically improving the response to selection. This proposal aims to streamline the selection of cultivated peanut breeding lines for early (Mycosphaerella arachidicola) and late (Nothopassalora personata) leaf spot resistance through genomic data development and genome-wide associations for validation and early generation, marker-assisted selection; the development of a high-throughput phenotyping platform for leaf spot differentiation and quantification; and the use of the generated phenotypic information in parallel with genomic prediction models in genetically stable family nurseries to improve on leaf spot resistance and other agronomically important traits to the crop.

In this project, we seek funds to develop a publically available genotyping platform for blueberry. Our long-term goal is to identify markers linked to desirable traits and to enable marker-assisted selection in blueberry (Vaccinium corymbosum) breeding programs. Our specific objectives are to: 1. Develop a publically available genotyping platform for genotyping blueberry germplasm and mapping populations at a low cost. 2. Phenotypically evaluate a diversity panel of 180 accessions of NC State blueberry germplasm collection as well as >80 species of Vaccinium sp. that are maintained at the National Clonal Germplasm Repository Corvallis, OR for various fruit quality-related traits. 3. Use the new genotyping platform to genotype the diversity panel in obj. 2 and a tetraploid blueberry mapping F2 population that is expected to segregate for fruit firmness and various fruit quality-related traits to validate the utility of markers.